Sustainable Electrical Discharge Machining of Nimonic C263 Superalloy

Abstract

Nimonic C263, a nickel-based superalloy, finds extensive application in manufacturing of complex parts in gas turbine, aircraft and automotive industries exclusively for exhaust sections. Machining of the alloy through traditional approach is a difficult task owing to its poor thermal conductivity, work hardening and high strength properties. However, the superalloy can be machined up to desired accuracy by non-traditional machining processes like electrical discharge machining (EDM). Yet again, machining of these difficult-to-machine materials not only consumes a significant amount of energy, but also produces unpleasant noise, hindering the sustainable aspect of the process. To meet the requisite of sustainable EDM and explore the machinability, an experimental investigation is proposed in this work, when Nimonic C263 superalloy is machined with three different electrodes, viz. copper, tungsten, and copper–tungsten. Outcomes of important process parameters, viz. voltage (U), discharge current (I), pulse-on-time (Ton), duty factor (τ), and electrode material, are studied on the responses, viz. specific energy consumption, machining noise (N), material removal rate, electrode wear rate, surface roughness (Ra) and radial overcut (C). Analysis of variance is conducted to analyse the influence of each parameter on the responses. A scanning electron microscope investigation is carried out to analyse the pre-machining and post-machining scenarios on the machined surface. The multiple responses are converted into single response by calculating the net outranking flow by application of preference ranking organization method for enrichment evaluation (PROMETHEE) approach. The PROMITHEE-based results are further improved by using a hybrid cuckoo search algorithm (combined PROMITHEE-based cuckoo search). A confirmative test is further conducted on the optimum machining conditions obtained by the hybrid approach (combination of PROMITHEE and cuckoo search) indicating an overall improvement of 6.02% for the responses, validating the proposed work.